Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

被引：112

作者：

Li, Jiawen ^{[1
]}

Yu, Tao ^{[1
]}

Zhang, Xiaoshun ^{[2
]}

Li, Fusheng ^{[1
]}

Lin, Dan ^{[1
]}

Zhu, Hanxin ^{[1
]}

机构：

[1] South China Univ Technol, Coll Elect Power, Guangzhou 510640, Peoples R China

[2] Shantou Univ, Coll Engn, Shantou 515063, Peoples R China

来源：

APPLIED ENERGY | 2021年 / 285卷

基金：

中国国家自然科学基金;

关键词：

Automatic generation control; Integrated energy system; Performance-based frequency regulation market; Efficient experience replay; Multiple experience pool replay twin delayed deep deterministic policy gradient; AUTOMATIC-GENERATION CONTROL; REINFORCEMENT; ALGORITHM;

D O I：

10.1016/j.apenergy.2020.116386

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

To balance the stochastic power disturbance in integrated energy system (IES), a novel automatic generation control (AGC) dispatch is proposed by taking account of the regulation rule that applies to a performance-based frequency regulation market, with the aim to reduce area control deviation and regulation mileage payment while complying with constraints of various regulation units. Thus, a multiple experience pool replay twin delayed deep deterministic policy gradient (MEPR-TD3) is put forward to improve the training efficiency and the action quality via four improvements including the multiple experience pool probability replay strategy. Finally, the performance of the proposed algorithm is verified on an extended two-area load frequency control (LFC) model and Hainan province IES with different demand of multiple energy.

引用

页数：17

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